• dplyr was used to manipulate the data
• ggplo2 was used to create figures
• tidyr was not used since the data was already arranged to desired standards

library(dplyr)
library(ggplot2)

• Mantis shrimps or stomatopods
• Marine crustaceans of the order Stomatopoda
• Some species have blunt forelimbs while others have sharp ones

• Appendages
• Telson

• During telson sparring, mantis shrimp take turns striking with their appendages against the telson of their opponent
• Individuals direct nearly every strike to the telson, and this is facilitated by the recipient assuming a coiled position referred to as a ‘telson coil’

data_1 <- read.csv("https://ndownloader.figshare.com/files/16851587")
knitr::kable(data_1)

Mantis Shrimp Shells

• Many types of animals develop morphological traits to partake in ritualized combat.
• Mantis shrimp present an interesting case in which their unique form of ritualized combat, termed telson sparring, may have coevolved with specialized morphological armour.

• The coefficient of restitution (COR), also denoted by (e), is the ratio of the final to initial relative velocity between two objects after they collide

• can also be viewed in terms of kinetic energy before and after a collision.

• e is usually a positive number between 0 and 1:

• e = 0: This is a perfectly inelastic collision. Kinetic energy is dissipated (converted to heat or work).

• 0 < e < 1: This is a real-world inelastic collision, in which some kinetic energy is dissipated.

• e = 1: This is a perfectly elastic collision, in which no kinetic energy is dissipated, and the objects rebound from one another with the same initial velocity.

• I mutated data_1 to add a new column of the ratio between the telson COR and abdomen COR
• lower ratio = telson and abdomen COR difference smaller
• higher ratio = telson and abdomen COR differece larger (telson COR is greater)
• I then separated the data into a "small" and "big" category by filtering the mass of the shrimp. This was done to create figures with clearer axis.

data_1_modified <- mutate(data_1, ratio = (cor.telson/cor.abdomen))
big_shrimps <- data_1_modified %>%
  group_by(species) %>%
  filter(mass > 15)
small_shrimps <- data_1_modified %>%
  group_by(species) %>%
  filter(mass < 15)

The figures created use mass on the x axis and the ratio of COR between the telson and the abdomen.

Geom_point and geom_smooth are used to create points and a trend line, the axis are then scaled to the minimum and maximum mass and ratio values for the small and big categories.

Then, facet_wrap is used to seaparate the graphs by species.

The purpose of this manipulation was to determine if there was a relationship between mass and ratio of COR telson vs abdomen across different species of mantis shrimp.